期刊名称：International Journal of Security and Its Applications
印刷版ISSN：1738-9976
出版年度：2016
卷号：10
期号：2
页码：237-246
DOI：10.14257/ijsia.2016.10.2.21
出版社：SERSC
摘要：In recent years ， machine learning method has been applied to the extensive research on traffic classification. In these methods, SVM (Support vector machine) is a supervised learning which can improve generalization ability of learning machine effectively. However, the penalty parameter C and kernel function parameter . are generally given by test experience during training of SVM. How to determine the optimal parameters of SVM is a problem to be solved. We proposed a method to deriving the optimal parameters of SVM based on GA (Genetic algorithm).This method does not need to traverse all the parameter points. The method extracts a certain number population from random solutions, and ultimately produces SVM optimal parameters according to the specific rules of operation. Through the method, we derived the optimal parameters combination C and . of SVM. The accuracy of network traffic classification is improved greatly.
关键词：Traffic classification; Genetic Algorithms; Support vector machine
